WO2018133003A1 - Procédé et système de reconstruction tridimensionnelle de tomodensitométrie - Google Patents

Procédé et système de reconstruction tridimensionnelle de tomodensitométrie Download PDF

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Publication number
WO2018133003A1
WO2018133003A1 PCT/CN2017/071694 CN2017071694W WO2018133003A1 WO 2018133003 A1 WO2018133003 A1 WO 2018133003A1 CN 2017071694 W CN2017071694 W CN 2017071694W WO 2018133003 A1 WO2018133003 A1 WO 2018133003A1
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dimensional
projection image
reconstructed
volume data
iteration
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PCT/CN2017/071694
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English (en)
Chinese (zh)
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陈垦
王澄
秦文健
熊璟
谢耀钦
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深圳先进技术研究院
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Priority to PCT/CN2017/071694 priority Critical patent/WO2018133003A1/fr
Publication of WO2018133003A1 publication Critical patent/WO2018133003A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Definitions

  • Embodiments of the present disclosure relate to image processing techniques, for example, to a Computed Tomography (CT) three-dimensional reconstruction method and system.
  • CT Computed Tomography
  • the cone beam CT device using the flat panel detector technology has advantages in imaging quality and imaging efficiency, so how to use the cone Fast and accurate CT 3D reconstruction under the beam CT model has become a very important issue.
  • FDK Feldkamp, Davis, Kress
  • the FDK algorithm is used as the filtered back projection algorithm.
  • the calculation speed is fast and the system resource requirements are low, the reconstructed image quality is poor, and there are artifacts in the image. Affects visualization and doctor diagnosis.
  • Algebraic Reconstruction Technique (ART), although it can solve the image quality problem of the filtered back projection algorithm, but ART needs to calculate the projection matrix, the calculation amount is large, the calculation efficiency is low, and the storage hardware is saved when the projection matrix is saved. The requirements are also high, limiting the clinical application of ART.
  • Embodiments of the present disclosure provide a CT three-dimensional reconstruction method and system, which can implement CT three-dimensional reconstruction.
  • an embodiment of the present disclosure provides a computed tomography CT three-dimensional reconstruction method, the method comprising:
  • each lower computer Controlling each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and storing the projection matrix in each memory of each lower computer;
  • the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is used as the CT three-dimensional reproduction result.
  • controlling each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image comprises:
  • each of the lower computers determines whether a line equation of each pixel intersects each voxel in the three-dimensional volume data to be reconstructed; if a line equation of the pixel points and a voxel in the three-dimensional volume data to be reconstructed Intersecting, obtaining a length value of a line connecting the line equations in the voxel; if the line equation of the pixel points does not intersect the voxel in the three-dimensional volume data to be reconstructed, a line connecting the line points of the line equation having a length value of 0 in the voxel;
  • controlling each of the lower computers to calculate a projection matrix of each pixel point in the received two-dimensional projection image comprises:
  • each lower computer is controlled to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation component, including :
  • N is the number of frames of the two-dimensional projection image
  • T i is the iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is the total number of pixels in the two-dimensional projection image
  • a ij is the projection matrix of the jth pixel point in the 2D projection image of the i-th frame
  • b ij is the pixel value of the jth pixel in the 2D projection image of the ith frame
  • x n is the current iteration state of the 3D volume data to be reconstructed in the nth iteration.
  • updating the current iteration state of the three-dimensional volume data to be reconstructed according to the iterative attenuation component returned by each lower-level machine includes:
  • Updating a current iteration state of the three-dimensional volume data to be reconstructed is x n+1 ;
  • x n+1 is the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration
  • ⁇ and ⁇ are preset weight values
  • R( ⁇ ) is a preset constraint term equation.
  • the two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source is sent to at least two lower computers, including at least one of the following:
  • Two-dimensional projection images at the same rotation angle are respectively sent to the at least two lower-level machines.
  • an embodiment of the present disclosure further provides a CT three-dimensional reconstruction system, where the system includes:
  • An image acquisition module configured to send a two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source to at least two lower-level machines;
  • a projection matrix receiving module configured to control each lower computer to calculate a projection matrix of each pixel in the received two-dimensional projection image, and store the projection matrix in each memory of each lower computer;
  • a component calculation module configured to control each of the lower computers to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation component ;
  • An iterative state update module configured to update a current iteration state of the three-dimensional volume data to be reconstructed according to the iterative attenuation component returned by each lower-level machine;
  • the iterative return module is set to determine whether the iteration termination condition is satisfied. If the iterative termination condition is not met, the return execution control is performed according to the projection matrix and the current iteration state of the 3D volume data to be reconstructed, and is calculated and received.
  • the two-dimensional projection image corresponds to an iteratively attenuating component and returns the operation of the iterative attenuation component until a predetermined iteration termination condition is satisfied;
  • the recurring result determining module is set to be the current iterative state of the three-dimensional volume data to be reconstructed after the iteration is terminated, if the iterative termination condition is satisfied, as the CT three-dimensional reproduction result.
  • the projection matrix receiving module is configured to: control each of the lower-level machines to construct each of the two-dimensional projection images of the X-ray source at the rotation angle according to the rotation angle corresponding to the received two-dimensional projection image The line equation of the pixel;
  • each of the lower computers determines whether a line equation of each pixel intersects with each voxel in the three-dimensional volume data to be reconstructed; if a line equation of each pixel is in the three-dimensional volume data to be reconstructed Obtaining a voxel, obtaining a length value of a line connecting the line equations of each pixel in the voxel; if the line equation of each pixel is in the three-dimensional volume data to be reconstructed If the voxels do not intersect, the length of the line connecting the pixel points is 0 in the voxel;
  • the projection matrix receiving module is configured to:
  • the component calculation module is configured to:
  • N is the number of frames of the two-dimensional projection image
  • T i is an iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is a pixel point of the two-dimensional projection image
  • a ij is the projection matrix of the jth pixel point in the 2D projection image of the i-th frame.
  • b ij is the pixel value of the jth pixel in the 2D projection image of the i-th frame
  • x n is the current iteration state of the 3D volume data to be reconstructed in the nth iteration.
  • the iterative state update module is configured to:
  • Updating a current iteration state of the three-dimensional volume data to be reconstructed is x n+1 ;
  • x n+1 is the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration
  • ⁇ and ⁇ are preset weight values
  • R( ⁇ ) is a preset constraint term equation.
  • the image acquisition module is set to at least one of the following:
  • Two-dimensional projection images at the same rotation angle are respectively sent to the at least two lower-level machines.
  • an embodiment of the present disclosure further provides a non-transitory computer readable storage medium storing computer executable instructions, the computer executable instructions being configured to perform the method described above.
  • an embodiment of the present disclosure further provides an electronic device, including:
  • At least one processor At least one processor
  • a memory communicatively coupled to the at least one processor, configured to store a program executable by the at least one processor
  • the program is executed by the at least one processor such that the at least one processor performs the CT three-dimensional reconstruction method described in the embodiments of the present disclosure.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by at least two lower computers, and the current iteration state of the three-dimensional volume data to be reconstructed is updated, and the iteration is terminated.
  • the current iterative state of the 3D volume data to be reconstructed is used as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency and the reusability of the calculation result.
  • FIG. 1 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 1 of the present disclosure
  • FIG. 2 is a schematic diagram of cone beam CT scanning in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of a three-dimensional volume data coordinate system and a projection data coordinate system in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure
  • FIG. 5 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 3 of the present disclosure.
  • FIG. 6 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 4 of the present disclosure.
  • FIG. 7 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 5 of the present disclosure.
  • FIG. 8 is a schematic diagram of a CT three-dimensional reconstruction system according to Embodiment 6 of the present disclosure.
  • Embodiment 9 is a structural diagram of an electronic device in Embodiment 8 of the present disclosure.
  • Embodiment 1 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 1 of the present disclosure. This embodiment is applicable to a case where a three-dimensional reconstruction is performed under a cone beam CT model, and the method may be performed by a CT three-dimensional reconstruction system.
  • the system can be implemented by software, hardware, or software and hardware, and the system can be integrated into a device such as a computer.
  • step 110 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X-ray source is transmitted to at least two lower-level machines.
  • CT is the electronic computed tomography technology, which can be divided into traditional CT, spiral CT and cone beam CT.
  • the cone beam CT has fast scanning speed, high image resolution and high radiation utilization rate.
  • the hundreds of tomographic scans of traditional CT and spiral CT are completed, and the resolution of the cone beam CT in the X, Y, and Z directions is consistent, which can reduce the volume effect commonly seen in traditional CT and spiral CT detection.
  • Present disclosure takes cone beam CT as an example to solve how to perform 3D reconstruction quickly and accurately under the cone beam CT model.
  • FIG. 2 is a schematic diagram of a cone beam CT scan in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure, wherein the rays I, J, K, and L are rays emitted by the X-ray source 210 and passing through the object to be measured 220, crossing the The X-ray cone beam of the object is received by the detector 230, and the obtained data is a two-dimensional projection image at the rotation angle.
  • the X-ray source rotates through an angle and performs the same operation until the scanning of the plurality of angles is completed.
  • the size of the rotation angle and the number of selections can be determined according to requirements.
  • the projection data is stored in an N*H*W three-dimensional array as unsigned short type data.
  • FIG. 3 is a schematic diagram of a three-dimensional volume data coordinate system and a projection data coordinate system in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure. Assuming that the light source rotates around the voxel X-axis, a two-dimensional projection image acquired at each rotation angle of the light source is obtained, and the two-dimensional projection image is sent to at least two lower-level machines.
  • the sending method may be that the two-dimensional projection image corresponding to the same rotation angle is sent to the same lower position machine, or the two-dimensional projection image corresponding to different rotation angles may be sent to the same lower position machine, and increasing the number of lower position machines may improve the data processing speed.
  • each lower computer is controlled to calculate a projection matrix of each pixel in the received two-dimensional projection image, and the projection matrix is stored in a memory of each of the lower computers.
  • A is the projection matrix of each pixel in the two-dimensional projection image
  • x is the three-dimensional volume data to be reconstructed
  • b is the pixel value of the projected image
  • R( ⁇ ) is the preset constraint term equation
  • ⁇ is the preset weight value. Iteratively finds x, takes the objective function F to the minimum value, and determines the three-dimensional volume data x to be reconstructed.
  • the factors that limit the application of ART algorithm in cone beam CT include: the calculation of projection matrix A is complicated; the data of positional relationship between all ray and three-dimensional volume data voxel in each frame projection is large, which requires high storage hardware and limits. The reusability of the calculation results.
  • the projection matrix A is stored in the respective lower computer, and the stored projection matrix A can be called in the subsequent cycle. At least two lower-level machines perform calculations at the same time, and the results are returned to the upper computer for accumulation, which can greatly speed up the calculation efficiency of the projection matrix.
  • each of the lower computers is controlled to calculate an iterative attenuation component corresponding to the two-dimensional projection image and return the iterative attenuation component according to the projection matrix and the current iteration state of the three-dimensional volume data to be reconstructed.
  • the iterative attenuation component is related to the current iteration state and the pixel value of the two-dimensional projection image.
  • the current iteration state is a numerical value, and the initial current iteration state may be 0, or may be a FDK algorithm.
  • the current iteration state is initially determined.
  • step 140 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration is updated by using the returned iterative attenuation component and the nth iteration state.
  • step 150 it is determined whether the iteration termination condition is satisfied. If the iteration termination condition is not satisfied, then step 130 is performed; if the iteration termination condition is satisfied, step 160 is performed.
  • the iterative termination condition may be that the number of iterations meets the preset number of iterations, or that the difference between the two successive iteration states satisfies the preset difference.
  • step 160 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • the current iterative state of the three-dimensional volume data to be reconstructed after the iteration is terminated can cause the objective function to reach a maximum value (or a minimum value), and the current iteration state at this time is determined as a CT three-dimensional reproduction result of the two-dimensional projection image.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by at least two lower computers, and the current iteration state of the three-dimensional volume data to be reconstructed is updated, and the iteration is terminated.
  • the current iterative state of the 3D volume data to be reconstructed is used as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency and the reusability of the calculation result.
  • FIG. 4 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 2 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 410 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X-ray source is transmitted to at least two lower-level machines.
  • each lower computer is controlled to construct a connection equation of the X-ray source and each pixel point in the two-dimensional projection image at the rotation angle according to the rotation angle corresponding to the received two-dimensional projection image.
  • step 430 the lower computer is controlled to determine whether the line equation of each pixel intersects each voxel in the three-dimensional volume data to be reconstructed; if the line equation of the pixel is in the three-dimensional volume data to be reconstructed When the voxels intersect, the length value of the connection of the line equation of the pixel in the voxel is obtained; if the image If the line equation of the prime point does not intersect with the voxel in the three-dimensional volume data to be reconstructed, the length of the connection of the line equation of the pixel is 0 in the voxel.
  • step 440 controlling, by each of the lower-level machines, a projection matrix of each pixel in the two-dimensional projection image according to a length value of a line connecting the line equations of each pixel in each voxel, And storing the projection matrix in the respective memory of the lower computer.
  • the voxel is the abbreviation of the volume element, which is suitable for the fields of three-dimensional imaging and medical imaging.
  • the voxel itself does not contain the data of the position in the space (ie the coordinates), and the voxel can represent a three-dimensional region with a constant scalar or vector. .
  • For each frame of the two-dimensional projection image if it is the first loop iteration process, calculate the connection equation of the X-ray source corresponding to the two-dimensional projection image of the frame and each pixel point in the two-dimensional projection image. Determining whether the connection equation intersects with each voxel in the three-dimensional volume data to be reconstructed.
  • connection equation of the pixel intersects the voxel in the three-dimensional volume data to be reconstructed, the connection of the connection equation of the pixel is calculated.
  • the length value of the portion within the voxel, and the three-dimensional position coordinates (x, y, z) of the voxel in the three-dimensional volume data are recorded.
  • the length of the connection equation of the pixel is 0 in the voxel.
  • the length value constitutes a projection matrix of the pixel points of the current frame two-dimensional projection image, and the projection matrix is stored in the respective memory of the lower computer.
  • the projection matrix can be calculated only once, and the stored projection matrix can be used in subsequent loop iterations, so if it is not the first iteration of the loop, the step of calculating the projection matrix is skipped.
  • each of the lower computers is controlled to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to a projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation component.
  • step 460 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • step 470 it is determined whether the preset iteration termination condition is satisfied. If the iteration termination condition is satisfied, step 480 is performed; if the iteration termination condition is not met, step 450 is returned.
  • step 480 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • the technical solution of the embodiment of the present disclosure calculates a line equation of a pixel point in a two-dimensional projection image corresponding to a two-dimensional projection image, according to a connection line equation of the pixel point and a three-dimensional volume data to be reconstructed.
  • the length value of the intersection of each voxel constructs the projection matrix of all the pixel points, and only one projection matrix can be calculated, and the projection matrix after the initial calculation is saved in the memory of the lower computer, and the subsequent cycle can be To use the stored projection matrix, the calculation process is simplified and the calculation efficiency is accelerated.
  • controlling, by each lower computer, the projection matrix of each pixel in the received two-dimensional projection image comprises:
  • the lower computer allocates one thread for each pixel point, and the projection matrix of each pixel point is distributedly calculated by at least two threads, and when all threads are executed, A projection matrix corresponding to all pixels of the current frame two-dimensional projection image is obtained.
  • the multi-threaded distributed computing method can improve the calculation speed and speed up the calculation efficiency of the projection matrix.
  • FIG. 5 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 3 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 510 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source is transmitted to at least two lower computers.
  • each lower computer is controlled to calculate a projection matrix of each pixel in the received two-dimensional projection image, and the projection matrix is stored in a memory of each of the lower computers.
  • each of the lower machines is controlled according to the formula: An iterative attenuation component corresponding to the received two-dimensional projection image is calculated.
  • N is the number of frames of the two-dimensional projection image
  • T i is the iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is the total number of pixels in the two-dimensional projection image
  • a ij is the projection matrix of the jth pixel point in the 2D projection image of the i-th frame
  • b ij is the pixel value of the jth pixel point in the 2D projection image of the ith frame
  • x n is the nth iteration Rebuild the current iteration state of the 3D volume data.
  • step 540 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • step 550 it is determined whether the preset iteration termination condition is satisfied. If the iteration termination condition is satisfied, step 560 is performed; if the iteration termination condition is not met, the execution 530 is returned.
  • step 560 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is performed. The result is reproduced for the CT three-dimensional.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by using multiple lower computers, and the current iteration state of the three-dimensional volume data to be reconstructed is updated, and the iteration is terminated.
  • the current iterative state of the reconstructed three-dimensional volume data is used as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency, and reusability of the calculation result.
  • FIG. 6 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 4 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 610 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X-ray source is transmitted to at least two lower-level machines.
  • each lower computer is controlled to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and the projection matrix is stored in a memory of the lower computer.
  • each of the lower machines is controlled according to the formula: An iterative attenuation component corresponding to each of the two-dimensional projection images is calculated.
  • step 640 the cumulative summation result of the iterative attenuation component corresponding to the two-dimensional projection image is used as the current iteration state of the three-dimensional volume data to be reconstructed and each of the two-dimensional projection images in the nth iteration.
  • the total attenuation between the iterations Sum is used as the cumulative summation result of the iterative attenuation component corresponding to the two-dimensional projection image as the current iteration state of the three-dimensional volume data to be reconstructed and each of the two-dimensional projection images in the nth iteration.
  • the lower computer sends the iterative attenuation component back to the upper computer, and the upper computer accumulates the iterative attenuation component corresponding to each frame of the two-dimensional projection image to obtain the iterative attenuation total Sum.
  • step 650 Updating the current iteration state of the three-dimensional volume data to be reconstructed is x n+1 .
  • x n+1 is the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration
  • ⁇ and ⁇ are preset weight values
  • R( ⁇ ) is a preset constraint term equation.
  • step 660 it is determined whether the iteration termination condition is satisfied. If the iteration termination condition is not satisfied, then step 630 is performed; if the iteration termination condition is satisfied, step 670 is performed.
  • step 670 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by at least two lower computers, and the three-dimensional volume data to be reconstructed is updated.
  • the current iterative state of the 3D volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency, and reusability of the calculation result.
  • FIG. 7 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 5 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 710 different two-dimensional projection images are separately sent to different lower computers.
  • different two-dimensional projection images are respectively sent to different lower-level machines, including at least one of the following:
  • Two-dimensional projection images at the same rotation angle are respectively sent to the at least two lower-level machines.
  • each lower computer uses a graphics processing unit (GPU) for parallel computing, processing only one frame of image.
  • GPU graphics processing unit
  • each lower computer is controlled to calculate a projection matrix of each pixel in the received two-dimensional projection image, and the projection matrix is stored in a memory of each of the lower computers.
  • step 730 the lower-level machine is controlled to calculate an iterative attenuation component corresponding to the received two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation. Component.
  • step 740 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • step 750 it is determined whether the preset iteration termination condition is satisfied. If the iteration termination condition is satisfied, step 760 is performed; if the iteration termination condition is not met, step 730 is returned.
  • step 760 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • multiple lower computers use multiple GPUs for parallel computing, and each lower computer processes only one frame of image, which reduces the amount of calculation and the amount of data, and improves the calculation efficiency.
  • Embodiment 6 uses multiple GPUs for parallel computing, and each lower computer processes only one frame of image, which reduces the amount of calculation and the amount of data, and improves the calculation efficiency.
  • FIG. 8 is a schematic diagram of a CT three-dimensional reconstruction system according to Embodiment 6 of the present disclosure.
  • the system includes an image acquisition module 810, an iterative state update module 840, an iterative return module 850, and a reproduction result determination module 860.
  • the image obtaining module 810, the iterative state updating module 840, the iterative returning module 850, and the reproduction result determining module 860 are all located in the upper computer.
  • the CT three-dimensional reconstruction system may further include a projection matrix receiving module 820 and a component calculation module 830, and the projection matrix receiving module 820 and the component calculation module 830 are located in the lower computer.
  • the image acquisition module 810 is configured to transmit the two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source to at least two lower computers.
  • the projection matrix receiving module 820 is configured to control each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and store the projection matrix in a memory of each of the lower computers.
  • the component calculation module 830 is configured to control the each lower computer to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return an iterative attenuation component.
  • the iterative state update module 840 is configured to update the current iteration state of the three-dimensional volume data to be reconstructed according to the iterative attenuation component returned by all lower-level machines.
  • the iterative returning module 850 is configured to determine whether the iterative termination condition is satisfied. If the iterative termination condition is not met, the execution execution control returns, according to the projection matrix stored in the memory, and the current iteration state of the 3D volume data to be reconstructed, The received iterative attenuation component corresponding to the two-dimensional projection image and returning the operation of the iterative attenuation component until the preset iteration termination condition is satisfied.
  • the recurring result determining module 860 is configured to set, as the iterative termination condition, the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated as the CT three-dimensional reproduction result.
  • the projection matrix receiving module 820 is configured to:
  • each of the lower computers determines whether a line equation of each pixel intersects each voxel in the three-dimensional volume data to be reconstructed; if the line equation of the pixel points intersects the voxel in the three-dimensional volume data to be reconstructed, a length value of a line connecting the line points in the voxel; if the line equation of the pixel point does not intersect the voxel in the three-dimensional volume data to be reconstructed, the line equation of the pixel point Connection in the office The length of the voxel is 0;
  • the projection matrix receiving module 820 is configured to:
  • the component calculation module 830 is configured to:
  • N is the number of frames of the two-dimensional projection image
  • T i is an iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is a pixel point of the two-dimensional projection image Total
  • a ij is the projection matrix of the jth pixel in the 2D projection image of the ith frame
  • b ij is the pixel value of the jth pixel in the 2D projection image of the ith frame
  • x n is the nth iteration The current iteration state of the 3D volume data to be reconstructed.
  • the iterative state update module 850 is configured to:
  • Updating a current iteration state of the three-dimensional volume data to be reconstructed is x n+1 ;
  • n n+1 is a current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration;
  • the ⁇ and ⁇ are preset weight values; and
  • the R( ⁇ ) is a preset constraint term equation; For the gradient operation.
  • the image obtaining module 810 is configured to:
  • the CT three-dimensional reconstruction system can perform the CT three-dimensional reconstruction method provided by any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of performing the CT three-dimensional reconstruction method.
  • Embodiments of the present disclosure also provide a non-transitory computer readable storage medium, the storage medium storing There are computer executable instructions that, when executed by a processor, can perform a CT three dimensional reconstruction method.
  • the method includes:
  • each lower computer Controlling each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and storing the projection matrix in each memory of each lower computer;
  • the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is used as the CT three-dimensional reproduction result.
  • the computer executable instructions when executed by a computer processor, can also be used to implement the technical solution of the CT three-dimensional reconstruction method provided by any embodiment of the present disclosure.
  • the present disclosure can be implemented by software and hardware, and of course can also be implemented by hardware.
  • the technical solution of the present disclosure may be embodied in the form of a software product, which may be stored in a non-transitory computer readable storage medium, such as a computer floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk or an optical disk, etc., the non-transitory computer readable storage medium comprising one or more instructions for causing a computer device (which may be a personal computer)
  • the server, or network device, etc. performs the method described in the embodiments of the present disclosure.
  • FIG. 9 a hardware structure diagram of an electronic device according to Embodiment 8 of the present disclosure is shown in FIG. 9 .
  • the electronic device includes:
  • At least one processor 910 such as one processor 910 in FIG. 9;
  • the electronic device may further include an input device 930 and an output device 940.
  • the processor 910, the memory 920, the input device 930, and the output device 940 in the electronic device may be connected by a bus or other means, as exemplified by a bus connection in FIG.
  • the memory 920 is a non-transitory computer readable storage medium, and can store a software program, a computer executable program, and a module, such as a program instruction or a module corresponding to the CT three-dimensional reconstruction method in the embodiment of the present application (for example, FIG. 8
  • the image acquisition module 810, the projection matrix receiving module 820, the component calculation module 830, the iterative state update module 840, the iterative return module 850, and the reproduction result determination module 860) are shown.
  • the processor 910 executes the functional application of the server and the data processing by executing software programs, instructions, and modules stored in the memory 920, that is, implementing the CT three-dimensional reconstruction method of the above method embodiment.
  • the memory 920 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the electronic device, and the like.
  • memory 920 can include high speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device.
  • memory 920 can optionally include memory remotely located relative to processor 910, which can be connected to the terminal device over a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • Input device 930 can be configured to receive input digital or character information and to generate key signal inputs related to user settings and function controls of the electronic device.
  • Output device 940 can include a display device such as a display screen.
  • the CT three-dimensional image reconstruction method and system provided by the embodiments of the present disclosure utilizes multiple lower-range machine distributed parallel computing to improve computational efficiency and reusability of calculation results.

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Abstract

La présente invention concerne un procédé et un système de reconstruction tridimensionnelle de tomodensitométrie. Le procédé consiste à : envoyer à au moins deux ordinateurs de niveau inférieur une image de projection bidimensionnelle obtenue par la réalisation d'une tomodensitométrie (CT) avec au moins deux angles de rotation d'une source de rayons X (210) (110) ; commander chacun des ordinateurs de niveau inférieur pour calculer une matrice de projection pour chaque point de pixel dans l'image de projection bidimensionnelle reçue, et sauvegarder la matrice de projection à des stockages internes respectifs de chacun des ordinateurs de niveau inférieur (120) ; commander chacun des ordinateurs de niveau inférieur pour calculer, sur la base de la matrice de projection et d'un état d'itération actuel de données de volume tridimensionnel à reconstruire, un composant de décroissance d'itération et renvoyer le composant de décroissance d'itération (130) ; mettre à jour, sur la base du composant de décroissance d'itération, l'état d'itération actuel (140) ; déterminer si une condition de fin d'itération est satisfaite (150), si la condition de fin d'itération n'est pas satisfaite, retourner alors pour commander chacun des ordinateurs de niveau inférieur pour la matrice de projection et l'état d'itération actuel afin de calculer le composant de décroissance d'itération et le renvoyer jusqu'à ce que la condition de fin d'itération prédéfinie soit satisfaite ; et si la condition de fin d'itération est satisfaite, alors utiliser l'état d'itération actuel après que l'itération se termine en tant que résultat de reproduction tridimensionnelle de tomodensitométrie (160).
PCT/CN2017/071694 2017-01-19 2017-01-19 Procédé et système de reconstruction tridimensionnelle de tomodensitométrie WO2018133003A1 (fr)

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